1. Field of the Invention
The present invention relates to a method for structuring a computing module for learning non-linear mapping which is generally a multivalued mapping. More specifically, the invention relates to a method for learning multivalued mapping by a functional approximation which enables learning to obtain an overview of the multifold structure of the mapping from a small amount of example data and also enables learning local changes.
2. Description of the Related Art
The inventor of the present invention proposed a method for multivalued function approximation (see Japanese Patent Application Laid Open (kokai) No. 7-93296, "Method for Learning Multivalued Function" and Transactions of the Institute of Electronics, Information and Communication Engineers of Japan A, vol. J78-A, No. 3, pp. 427-439 (1995) "Multivalued Regularization Network").
However, in the above-mentioned method for multivalued function approximation, only locally defined base functions are used, although multivalued functions can be expressed. Namely, F.sub.k is expressed by a function of a linear sum of the local base functions centering on t.sub.kp, as follows: ##EQU1##
The above mentioned Japanese Patent Application Laid Open No. 7-93296 describes an example wherein the center t.sub.kp coincides with the input portion x.sub.(i) of the teaching data.
However, in the above-mentioned method for multivalued function approximation, since base functions are defined only in the vicinity of the teaching data or the center t.sub.kp, functions are not defined where the teaching data do not exist. Another problem is that when the numbers of input and output space dimensions (m+n) become large the required amount of teaching data remarkably increases.